OpenRaiser/Intern-Atlas huggingface dataset:
Fast, Trainable, Multiscale Denoising
Venue: arXiv cs.CV 2018
Year: 2018

Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows real-time applications on low-powered devices. The key idea is to learn a set of kernels that upscales, filters, and blends patches of different scales guided by local structure analysis. This approach is trainable so that learned filters are capable of treating diverse noise patterns and artifacts. Experimental results show that the presented approach produces comparable results to state-of-the-art algorithms while processing time is orders of magnitude faster.
Reactive Reinforcement Learning in Asynchronous Environments
Venue: arXiv cs.AI 2018
Year: 2018

The relationship between a reinforcement learning (RL) agent and an asynchronous environment is often ignored. Frequently used models of the interaction between an agent and its environment, such as Markov Decision Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the fact that, in an asynchronous environment, the state of the environment may change during computation performed by the agent. In an asynchronous environment, minimizing reaction time---the time it takes for an agent to react to an observation---also minimizes the time in which the state of the environment may change following observation. In many environments, the reaction time of an agent directly impacts task performance by permitting the environment to transition into either an undesirable terminal state or a state where performing the chosen action is inappropriate. We propose a class of reactive reinforcement learning algorithms that address this problem of asynchronous environments by immediately acting after observing new state information. We compare a reactive SARSA learning algorithm with the conventional SARSA learning algorithm on two asynchronous robotic tasks (emergency stopping and impact prevention), and show that the reactive RL algorithm reduces the reaction time of the agent by approximately the duration of the algorithm's learning update. This new class of reactive algorithms may facilitate safer control and faster decision making without any change to standard learning guarantees.
Virtual Immersive Reality for Stated Preference Travel Behaviour Experiments: A Case Study of Autonomous Vehicles on Urban Roads
Venue: arXiv cs.HC 2018
Year: 2018

Stated preference experiments have been known to suffer from the lack of realism. This issue is particularly visible when the scenario doesn't have a well understood prior reference e.g. in case of the autonomous vehicles related scenarios. We present Virtual Immersive Reality Environment (VIRE) that is capable of developing highly realistic, immersive, and interactive choice scenario. We demonstrate the use of VIRE in the pedestrian preferences related to autonomous vehicles and associated infrastructure changes on urban streets of Montréal. The results are compared with predominantly used approaches i.e. text-only and visual aid. We show that VIRE results in better understanding of the scenario and consistent results.
Towards Realisation of Heterogeneous Earth-Observation Sensor Database Framework for the Sensor Observation Service based on PostGIS
Venue: arXiv cs.DB 2018
Year: 2018

Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote Geosensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic services such as the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the network before they are fused and processed on the service middleware. This process is very massive and unnecessary communication-work load on the service middleware. Massive work load in large raster downloads from flat-file raster data sources each time a request is made and huge integration and geo-processing work load on the service middleware which could actually be better leveraged at the database This paper therefore proposes the realization of heterogeneous sensor database framework based on PostGIS for integration, geo-processing and spatial analysis of remote and in-situ sensor observations at the database level. Also discussed in this paper is how the framework can be integrated in the Sensor Observation Service (SOS) to reduce communication and massive workload on the Geospatial Web Services and as well make query request from the user end a lot more flexible. Keywords: Earth-Observation, Heterogeneous Earth-Observation Sensor Database, PostGIS , Sensor Observation Service.
High Speed SRT Divider for Intelligent Embedded System
Venue: arXiv cs.AR 2018
Year: 2018

Increasing development in embedded systems, VLSI and processor design have given rise to increased demands from the system in terms of power, speed, area, throughput etc. Most of the sophisticated embedded system applications consist of processors, which now need an arithmetic unit with the ability to execute complex division operations with maximum efficiency. Hence the speed of the arithmetic unit is critically dependent on division operation. Most of the dividers use the SRT division algorithm for division. In IoT and other embedded applications, typically radix 2 and radix 4 division algorithms are used. The proposed algorithm lies on parallel execution of various steps so as to reduce time critical path, use fuzzy logic to solve the overlap problem in quotient selection, hence reducing maximum delay and increasing the accuracy. Every logical circuit has a maximum delay on which the timing of the circuit is dependent and the path, causing the maximum delay is known as the critical path. Our approach uses the previous SRT algorithm methods to make a highly parallel pipelined design and use Mamdani model to determine a solution to the overlapping problem to reduce the overall execution time of radix 4 SRT division on 64 bits double precision floating point numbers to 281ns. The design is made using Bluespec System Verilog, synthesized and simulated using Vivado v.2016.1 and implemented on Xilinx Virtex UltraScale FPGA board.
Approximate Set Union Via Approximate Randomization
Venue: arXiv cs.DS 2018
Year: 2018

We develop an randomized approximation algorithm for the size of set union problem $\arrowvert A_1\cup A_2\cup...\cup A_m\arrowvert$, which given a list of sets $A_1,...,A_m$ with approximate set size $m_i$ for $A_i$ with $m_i\in \left((1-β_L)|A_i|, (1+β_R)|A_i|\right)$, and biased random generators with $Prob(x=\randomElm(A_i))\in \left[{1-α_L\over |A_i|},{1+α_R\over |A_i|}\right]$ for each input set $A_i$ and element $x\in A_i,$ where $i=1, 2, ..., m$. The approximation ratio for $\arrowvert A_1\cup A_2\cup...\cup A_m\arrowvert$ is in the range $[(1-ε)(1-α_L)(1-β_L), (1+ε)(1+α_R)(1+β_R)]$ for any $ε\in (0,1)$, where $α_L, α_R, β_L,β_R\in (0,1)$. The complexity of the algorithm is measured by both time complexity, and round complexity. The algorithm is allowed to make multiple membership queries and get random elements from the input sets in one round. Our algorithm makes adaptive accesses to input sets with multiple rounds. Our algorithm gives an approximation scheme with $O(\setCount\cdot(\log \setCount)^{O(1)})$ running time and $O(\log m)$ rounds, where $m$ is the number of sets. Our algorithm can handle input sets that can generate random elements with bias, and its approximation ratio depends on the bias. Our algorithm gives a flexible tradeoff with time complexity $O\left(\setCount^{1+ξ}\right)$ and round complexity $O\left({1\over ξ}\right)$ for any $ξ\in(0,1)$.
Sentiment Analysis on Speaker Specific Speech Data
Venue: arXiv cs.CL 2018
Year: 2018

Sentiment analysis has evolved over past few decades, most of the work in it revolved around textual sentiment analysis with text mining techniques. But audio sentiment analysis is still in a nascent stage in the research community. In this proposed research, we perform sentiment analysis on speaker discriminated speech transcripts to detect the emotions of the individual speakers involved in the conversation. We analyzed different techniques to perform speaker discrimination and sentiment analysis to find efficient algorithms to perform this task.
Design and Implementation of the Andromeda Proof Assistant
Venue: arXiv cs.LO 2018
Year: 2018

Andromeda is an LCF-style proof assistant where the user builds derivable judgments by writing code in a meta-level programming language AML. The only trusted component of Andromeda is a minimalist nucleus (an implementation of the inference rules of an object-level type theory), which controls construction and decomposition of type-theoretic judgments. Since the nucleus does not perform complex tasks like equality checking beyond syntactic equality, this responsibility is delegated to the user, who implements one or more equality checking procedures in the meta-language. The AML interpreter requests witnesses of equality from user code using the mechanism of algebraic operations and handlers. Dynamic checks in the nucleus guarantee that no invalid object-level derivations can be constructed. %even if the AML code (or interpreter) is untrusted. To demonstrate the flexibility of this system structure, we implemented a nucleus consisting of dependent type theory with equality reflection. Equality reflection provides a very high level of expressiveness, as it allows the user to add new judgmental equalities, but it also destroys desirable meta-theoretic properties of type theory (such as decidability and strong normalization). The power of effects and handlers in AML is demonstrated by a standard library that provides default algorithms for equality checking, computation of normal forms, and implicit argument filling. Users can extend these new algorithms by providing local "hints" or by completely replacing these algorithms for particular developments. We demonstrate the resulting system by showing how to axiomatize and compute with natural numbers, by axiomatizing the untyped $λ$-calculus, and by implementing a simple automated system for managing a universe of types.
Generating Python Code From Object-Z Specifications
Venue: arXiv cs.SE 2018
Year: 2018

Object-Z is an object-oriented specification language which extends the Z language with classes, objects, inheritance and polymorphism that can be used to represent the specification of a complex system as collections of objects. There are a number of existing works that mapped Object-Z to C++ and Java programming languages. Since Python and Object-Z share many similarities, both are object-oriented paradigm, support set theory and predicate calculus moreover, Python is a functional programming language which is naturally closer to formal specifications, we propose a mapping from Object-Z specifications to Python code that covers some Object-Z constructs and express its specifications in Python to validate these specifications. The validations are used in the mapping covered preconditions, post-conditions, and invariants that are built using lambda function and Python's decorator. This work has found Python is an excellent language for developing libraries to map Object-Z specifications to Python.
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces
Venue: arXiv cs.LG 2018
Year: 2018

We introduce several new black-box reductions that significantly improve the design of adaptive and parameter-free online learning algorithms by simplifying analysis, improving regret guarantees, and sometimes even improving runtime. We reduce parameter-free online learning to online exp-concave optimization, we reduce optimization in a Banach space to one-dimensional optimization, and we reduce optimization over a constrained domain to unconstrained optimization. All of our reductions run as fast as online gradient descent. We use our new techniques to improve upon the previously best regret bounds for parameter-free learning, and do so for arbitrary norms.
The Dangerous Dogmas of Software Engineering
Venue: arXiv cs.SE 2018
Year: 2018

To legitimize itself as a scientific discipline, the software engineering academic community must let go of its non-empirical dogmas. A dogma is belief held regardless of evidence. This paper analyzes the nature and detrimental effects of four software engineering dogmas - 1) the belief that software has "requirements"; 2) the division of software engineering tasks into analysis, design, coding and testing; 3) the belief that software engineering is predominantly concerned with designing "software" systems; 4) the belief that software engineering follows methods effectively. Deconstructing these dogmas reveals that they each oversimplify and over-rationalize aspects of software engineering practice, which obscures underlying phenomena and misleads researchers and practitioners. Evidenced-based practice is analyzed as a means to expose and repudiate non-empirical dogmas. This analysis results in several novel recommendations for overcoming the practical challenges of evidence-based practice.
Linear-Time Algorithm for Long LCF with $k$ Mismatches
Venue: arXiv cs.DS 2018
Year: 2018

In the Longest Common Factor with $k$ Mismatches (LCF$_k$) problem, we are given two strings $X$ and $Y$ of total length $n$, and we are asked to find a pair of maximal-length factors, one of $X$ and the other of $Y$, such that their Hamming distance is at most $k$. Thankachan et al. show that this problem can be solved in $\mathcal{O}(n \log^k n)$ time and $\mathcal{O}(n)$ space for constant $k$. We consider the LCF$_k$($\ell$) problem in which we assume that the sought factors have length at least $\ell$, and the LCF$_k$($\ell$) problem for $\ell=Ω(\log^{2k+2} n)$, which we call the Long LCF$_k$ problem. We use difference covers to reduce the Long LCF$_k$ problem to a task involving $m=\mathcal{O}(n/\log^{k+1}n)$ synchronized factors. The latter can be solved in $\mathcal{O}(m \log^{k+1}m)$ time, which results in a linear-time algorithm for Long LCF$_k$. In general, our solution to LCF$_k$($\ell$) for arbitrary $\ell$ takes $\mathcal{O}(n + n \log^{k+1} n/\sqrt{\ell})$ time.
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation
Venue: arXiv cs.LG 2018
Year: 2018

Mild cognitive impairment (MCI) is a prodromal phase in the progression from normal aging to dementia, especially Alzheimers disease. Even though there is mild cognitive decline in MCI patients, they have normal overall cognition and thus is challenging to distinguish from normal aging. Using transcribed data obtained from recorded conversational interactions between participants and trained interviewers, and applying supervised learning models to these data, a recent clinical trial has shown a promising result in differentiating MCI from normal aging. However, the substantial amount of interactions with medical staff can still incur significant medical care expenses in practice. In this paper, we propose a novel reinforcement learning (RL) framework to train an efficient dialogue agent on existing transcripts from clinical trials. Specifically, the agent is trained to sketch disease-specific lexical probability distribution, and thus to converse in a way that maximizes the diagnosis accuracy and minimizes the number of conversation turns. We evaluate the performance of the proposed reinforcement learning framework on the MCI diagnosis from a real clinical trial. The results show that while using only a few turns of conversation, our framework can significantly outperform state-of-the-art supervised learning approaches.
Lifting Private Information Retrieval from Two to any Number of Messages
Venue: arXiv cs.IT 2018
Year: 2018

We study private information retrieval (PIR) on coded data with possibly colluding servers. Devising PIR schemes with optimal download rate in the case of collusion and coded data is still open in general. We provide a lifting operation that can transform what we call one-shot PIR schemes for two messages into schemes for any number of messages. We apply this lifting operation on existing PIR schemes and describe two immediate implications. First, we obtain novel PIR schemes with improved download rate in the case of MDS coded data and server collusion. Second, we provide a simplified description of existing optimal PIR schemes on replicated data as lifted secret sharing based PIR.
Structured Label Inference for Visual Understanding
Venue: arXiv cs.CV 2018
Year: 2018

Visual data such as images and videos contain a rich source of structured semantic labels as well as a wide range of interacting components. Visual content could be assigned with fine-grained labels describing major components, coarse-grained labels depicting high level abstractions, or a set of labels revealing attributes. Such categorization over different, interacting layers of labels evinces the potential for a graph-based encoding of label information. In this paper, we exploit this rich structure for performing graph-based inference in label space for a number of tasks: multi-label image and video classification and action detection in untrimmed videos. We consider the use of the Bidirectional Inference Neural Network (BINN) and Structured Inference Neural Network (SINN) for performing graph-based inference in label space and propose a Long Short-Term Memory (LSTM) based extension for exploiting activity progression on untrimmed videos. The methods were evaluated on (i) the Animal with Attributes (AwA), Scene Understanding (SUN) and NUS-WIDE datasets for multi-label image classification, (ii) the first two releases of the YouTube-8M large scale dataset for multi-label video classification, and (iii) the THUMOS'14 and MultiTHUMOS video datasets for action detection. Our results demonstrate the effectiveness of structured label inference in these challenging tasks, achieving significant improvements against baselines.
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
Venue: arXiv cs.CV 2018
Year: 2018

Object detection is a major challenge in computer vision, involving both object classification and object localization within a scene. While deep neural networks have been shown in recent years to yield very powerful techniques for tackling the challenge of object detection, one of the biggest challenges with enabling such object detection networks for widespread deployment on embedded devices is high computational and memory requirements. Recently, there has been an increasing focus in exploring small deep neural network architectures for object detection that are more suitable for embedded devices, such as Tiny YOLO and SqueezeDet. Inspired by the efficiency of the Fire microarchitecture introduced in SqueezeNet and the object detection performance of the single-shot detection macroarchitecture introduced in SSD, this paper introduces Tiny SSD, a single-shot detection deep convolutional neural network for real-time embedded object detection that is composed of a highly optimized, non-uniform Fire sub-network stack and a non-uniform sub-network stack of highly optimized SSD-based auxiliary convolutional feature layers designed specifically to minimize model size while maintaining object detection performance. The resulting Tiny SSD possess a model size of 2.3MB (~26X smaller than Tiny YOLO) while still achieving an mAP of 61.3% on VOC 2007 (~4.2% higher than Tiny YOLO). These experimental results show that very small deep neural network architectures can be designed for real-time object detection that are well-suited for embedded scenarios.
EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method. Our proposed approximate Hessian matrix is memory-efficient and can be applied to any FCNNs where the activation and criterion functions are twice differentiable. We devise a CG-based method incorporating one-rank approximation to derive Newton directions for training FCNNs, which significantly reduces both space and time complexity. This CG-based method can be employed to solve any linear equation where the coefficient matrix is Kronecker-factored, symmetric and positive definite. Empirical studies show the efficacy and efficiency of our proposed method.
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Venue: arXiv cs.LG 2018
Year: 2018

Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by these studies to address high dimensionality and small cohort size challenges. However, most existing MTL approaches are based on linear models and suffer from two major limitations: 1) they cannot explicitly consider upper/lower bounds in these clinical scores; 2) they lack the capability to capture complicated non-linear interactions among the variables. In this paper, we propose Subspace Network, an efficient deep modeling approach for non-linear multi-task censored regression. Each layer of the subspace network performs a multi-task censored regression to improve upon the predictions from the last layer via sketching a low-dimensional subspace to perform knowledge transfer among learning tasks. Under mild assumptions, for each layer the parametric subspace can be recovered using only one pass of training data. Empirical results demonstrate that the proposed subspace network quickly picks up the correct parameter subspaces, and outperforms state-of-the-arts in predicting neurodegenerative clinical scores using information in brain imaging.
Heron Inference for Bayesian Graphical Models
Venue: arXiv cs.LG 2018
Year: 2018

Bayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields. Current inference methods primarily follow different kinds of trade-offs between computational complexity and predictive accuracy. At one end of the spectrum, variational inference approaches perform well in computational efficiency, while at the other end, Gibbs sampling approaches are known to be relatively accurate for prediction in practice. In this paper, we extend an existing Gibbs sampling method, and propose a new deterministic Heron inference (Heron) for a family of Bayesian graphical models. In addition to the support for nontrivial distributability, one more benefit of Heron is that it is able to not only allow us to easily assess the convergence status but also largely improve the running efficiency. We evaluate Heron against the standard collapsed Gibbs sampler and state-of-the-art state augmentation method in inference for well-known graphical models. Experimental results using publicly available real-life data have demonstrated that Heron significantly outperforms the baseline methods for inferring Bayesian graphical models.